To obtain better well performance and improved production from shale gas reservoirs, it is important to understand the behavior of shale gas wells and to identify different flow regions in them over a period of time. It is also important to understand best fracture and stimulation practice to increase productivity of wells. These objectives require that accurate production analysis be performed. For accurate production analysis, it is important to analyze the production behavior of wells, and field production data should be interpreted in such a way that it will identify well parameters. This can be done by performing a detailed analysis on a number of wells over whole reservoirs. This study is an approach that will lead to identifying different flow regions in shale gas wells that include linear and bilinear flow. Important field parameters can be calculated from those observations to help improve future performance. The detailed plots of several wells in this study show some good numbers for linear and bilinear flow, and some unique observations were made. The purpose of this work is to also manage the large amount of data in such a way that they can be used with ease for future studies. A program was developed to automate the analysis and generation of different plots. The program can also be used to perform the simple calculations to calculate different parameters. The goal was to develop a friendly user interface that would facilitate reservoir analysis. Examples were shown for each flow period, i.e. linear and bilinear flow. Different plots were generated (e.g; Bob Plot (square root of time plot) and Fourth Root of Time Plot, that will help in measuring slopes and thus reservoir parameters such as fracture permeability and drainage area. Different unique cases were also observed that show a different behavior of well in one type of plot from another.

As the conventional reservoirs decrease in discovering, producing and reserving, unconventional reservoirs are more remarkable in terms of discovering, development and having more reserve. More fields have been discovered where Barnett Shale and Bakken Shale are the most recently unconventional reservoir examples. Shale reservoirs are typically considered self-sourcing and have very low permeability ranging from 10-100 nanodarcies. Over the past few decades, numerous research projects and developments have been studied, but it seems there is still some contention and misunderstanding surrounding shale reservoirs. One of the largest shale in the United State is the Bakken Shale play. This study will describe the primary geologic characteristics, field development history, reservoir properties,and especially production trends, over the Bakken Shale play. Data are available for over hundred wells from different companies. Most production data come from the Production Data Application (HDPI) database and in the format of monthly production for oil, water and gas. Additional 95 well data including daily production rate, completion, Pressure Volume Temperature (PVT), pressure data are given from companies who sponsor for this research study. This study finds that there are three Types of well production trends in the Bakken formation. Each decline curve characteristic has an important meaning to the production trend of the Bakken Shale play. In the Type I production trend, the reservoir pressure drops below bubble point pressure and gas releasingout of the solution. With the Type II production trend, oil flows linearly from the matrix into the fracture system, either natural fracture or hydraulic fracture. Reservoir pressure is higher than the bubble point pressure during the producing time and oil flows as a single phase throughout the production period of the well. A Type III production trend typically has scattering production data from wells with a different Type of trend. It is difficult to study this Type of behavior because of scattering data, which leads to erroneous interpretation for the analysis. These production Types, especially Types I and II will give a new type curve matches for shale oil wells above or below the bubble point.

The main goal this master thesis is the identification of key factors influencing the performance and production of Czech agriculture. The work contains into three parts. First part summarizes theoretically background concepts for construction models with looking historical contexts. The second part builds a data base for another work. The third part of the thesis is computational, formulates specific numerical models of possible answer. This part contains a discussion of the results and definitions of possible limits.